Capability-Driven Data Infrastructure Proposed for Generalist Image Generation
A recent preprint on arXiv (2608.18076) introduces a data infrastructure focused on capabilities for generating generalist images, tackling the difficulties of assembling task-specific datasets and managing diverse supervision. This infrastructure includes the construction of capability-specific supervision and a curriculum that aligns with these capabilities, featuring three interconnected data engines: text-image grounding, inter-image transformation, and image-knowledge association. Experts in captioning coordinate supervision across various tasks, while a multi-phase curriculum adapts task composition, visual concept distribution, data quality, and image resolution throughout the training process. This framework merges data creation with curriculum scheduling, highlighting capability interdependencies for effective training of generalist models, making it significant for those working in foundation models, data-centric AI, and image generation.
Key facts
- Paper presents capability-driven data infrastructure for generalist image generation
- Conventional pipelines optimize task-specific datasets in isolation, a limitation addressed
- Infrastructure couples capability-specific supervision construction with capability-aligned curriculum scheduling
- Includes three data engines: text-image grounding, inter-image transformation, image-knowledge association
- Caption experts align T2I and editing supervision across tasks and granularities
- Multi-stage curriculum evolves task composition, visual-concept distribution, data quality, and image resolution
- Published on arXiv with ID 2608.18076 as a cross announcement
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